Simulating a local emissions levy to fund local energy effiency retrofit

‘Wider Solent’ as a case study

1 About

Paper prepared for the 2021 International Conference on Evolving Cities, University of Southampton, 22 – 24 September 2021.

2 Citation

Anderson, B. (2021). Simulating the consequences of an emissions levy at the city and neighbourhood scale. Paper presented at International Conference on Evolving Cities, Southampton, United Kingdom

3 Introduction

Background blurb about emissions, retofit, carbon tax/levy etc

4 Emissions Levy Case Study - Wider Solent

In the reminder of this paper we develop a model of an emissions levy using LSOA level data on emissions derived from the CREDS place-based emissions calculator. We apply carbon ‘values’ to a number of emissions categories to estimate the levy revenue that would be generated for each LSOA in year 1 of such a levy. We then sum these values to given an overall levy revenue estimate for the area in the case study.

We then use estimates of the cost of retrofitting EPC band dwellings D-E and F-G together with estimates of the number of such dwellings in each of the LSOAs to calculate the likely cost of such upgrades in each LSOA and for the whole area in the case study.

We then compare the distributions of the two to understand whether sufficient revenue would be generated within each LSOA or in the case styudy area to enable the per-LSOA or whole case study area costs of the energy efficiency upgrades to be met. In doing so we also analyse the extent to which redistribution of revenue from high emissions areas (households) would be required. It should be noted that this is area level analysis using mean emissions per household. It will not capture within-LSOA hetergeneity in emissions and so will almost certaonly underestimate the range of the household level emissions levy value.

4.1 Data and boundary files

We will use a number of datasets to analyse the patterns of emissions. Some of these are in the repo as they are public access, others are not (or too large).

This analysis is at LSOA level.

4.1.1 Useful LSOA labels and codes

Load LSOA look-up table and list the local authroities we are covering.

## Loading LSOA look-up table with useful labels

## [1] "LSOA11CD"        "LSOA11NM"        "MSOA11NM"        "LA11NM"         
## [5] "WD20CD"          "WD20NM"          "RUC11"           "Supergroup Name"
## [9] "LAD11NM"

## Number of LSOAs covered

## [1] 1194

## Number of local authorities covered

## [1] 14

## Using 'n_LSOAs' as value column. Use 'value.var' to override

4.1.2 Boundaries

LSOA - this is all going to be LSOA analysis

## Loading Solent LSOA boundaries from file

## Rows of data: 1136

Check with a map…

## Boundary data co-ord system: 27700

Figure 4.1: LSOA check map (shows LSOA, MSOA and ward names when clicked

4.1.3 IMD 2019

Labeled as 2019 but actually 2018 data. Source: https://www.gov.uk/government/statistics/english-indices-of-deprivation-2019

## Overall IMD decile counts

## [1] 32844

## 
##   1 (10% most deprived)                       2                       3 
##                    3284                    3284                    3285 
##                       4                       5                       6 
##                    3284                    3285                    3284 
##                       7                       8                       9 
##                    3284                    3285                    3284 
## 10 (10% least deprived) 
##                    3285

## # Solent IMD decile counts

## [1] 1194

## 
##   1 (10% most deprived)                       2                       3 
##                      44                      81                      94 
##                       4                       5                       6 
##                     122                     115                     103 
##                       7                       8                       9 
##                     122                     134                     159 
## 10 (10% least deprived) 
##                     220

## 
##   1 (10% most deprived)                       2                       3 
##              0.03685092              0.06783920              0.07872697 
##                       4                       5                       6 
##              0.10217755              0.09631491              0.08626466 
##                       7                       8                       9 
##              0.10217755              0.11222781              0.13316583 
## 10 (10% least deprived) 
##              0.18425461

## 
## 50% least deprived  50% most deprived 
##                738                456

## 
## 50% least deprived  50% most deprived 
##          0.6180905          0.3819095

These are LSOA level deprivation indices. Decile is the English & Welsh decile:

• 1 = 10% most deprived LSOAs in England & Wales;
• 10 = 10% least deprived LSOA in England & Wales.

Figure 4.2: LSOA IMD map (shows LSOA, MSOA, ward names and IMD decile when clicked

4.1.4 Fuel poverty

2019 estimates - do we actually use this data?

Source: https://www.gov.uk/government/statistics/sub-regional-fuel-poverty-data-2021

4.2 CREDS place-based emmissions estimates

See https://www.creds.ac.uk/why-we-built-a-place-based-carbon-calculator/

“The highest carbon areas have an average per person footprint more than eight times larger than the lowest carbon areas.”

“We are not effectively targeting decarbonisation policies in high carbon areas. For example, the recently collapsed Green Homes Grants scheme provided a grant to cover 66% of the cost (up to £5,000) of retrofitting homes. For people claiming certain benefits, the cap was raised to 100% and £10,000. But the calculator shows that the big polluters are the large homes in very wealthy areas. In these neighbourhoods, the issue is not affordability but motivation. For high income households, energy costs are a small proportion of their expenditure and so the cost savings for retrofitting their home are inconsequential. As there are no policy “sticks” to incentivise action in the collective interest it is unsurprising that high carbon neighbourhoods have not prioritised decarbonisation."

Source: https://www.carbon.place/

Notes:

• Emissions are presented as per capita…
• Appears to be based on residential/citizen emissions only - does not appear to include commercial/manufacturing/land use etc

## [1] 32844

Table 4.2: Data summary

Name	credsLsoaDT
Number of rows	1194
Number of columns	30
Key	LSOA11CD
_______________________
Column type frequency:
character	7
factor	1
numeric	22
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
LAD11NM	0	1	4	21	0	14	0
WD18NM	0	1	4	42	0	292	0
LSOA11CD	0	1	9	9	0	1194	0
LSOA11NM	0	1	9	26	0	1194	0
WD20CD	0	1	9	9	0	282	0
RUC11	0	1	19	27	0	3	0
oacSuperGroupName	0	1	15	35	0	8	0

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
IMD_Decile_label	0	1	FALSE	10	10 : 220, 9: 159, 8: 134, 4: 122

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
CREDStotal_kgco2e	0	1.00	15183049.41	5154042.47	4818400.00	11606400.00	14731500.00	18590000.00	37236000.00	▅▇▅▁▁
CREDSgas_kgco2e2018	0	1.00	1543093.13	601806.98	0.00	1199077.50	1495220.00	1849125.00	5108580.00	▂▇▂▁▁
CREDSelec_kgco2e2018	0	1.00	782210.06	266915.81	350200.00	613800.00	714560.00	872990.00	2478420.00	▇▃▁▁▁
CREDSotherEnergy_kgco2e2011	0	1.00	136491.84	318307.76	0.00	22221.50	40244.50	76161.25	2315490.00	▇▁▁▁▁
CREDSmeteredHomeEnergy_kgco2e2018	0	1.00	2325303.19	701431.75	622710.00	1865605.00	2212690.00	2657880.00	7587000.00	▅▇▁▁▁
CREDSallHomeEnergy_kgco2e2018	0	1.00	2461795.03	754979.54	1048950.00	1963222.50	2321827.00	2811340.00	8553640.00	▇▅▁▁▁
CREDScar_kgco2e2018	0	1.00	1861521.96	714842.21	333540.00	1357290.00	1768650.00	2237750.00	5014000.00	▃▇▃▁▁
CREDSvan_kgco2e2018	0	1.00	305585.01	898342.37	11328.00	126901.25	183270.00	278405.00	18232800.00	▇▁▁▁▁
pop_2018	0	1.00	1663.18	391.28	947.00	1430.00	1590.00	1780.00	5620.00	▇▂▁▁▁
energy_pc	28	0.98	17.39	5.40	6.67	13.52	16.40	20.47	44.48	▅▇▂▁▁
pc_Heating_Electric	0	1.00	9.53	9.42	0.13	3.48	6.37	12.52	85.27	▇▁▁▁▁
epc_total	0	1.00	422.65	180.14	153.00	309.00	376.00	492.00	2350.00	▇▁▁▁▁
epc_newbuild	0	1.00	80.15	127.29	1.00	27.00	46.00	80.00	1960.00	▇▁▁▁▁
epc_A	0	1.00	0.78	3.15	0.00	0.00	0.00	0.00	48.00	▇▁▁▁▁
epc_B	0	1.00	56.91	114.81	0.00	7.00	21.00	60.00	1800.00	▇▁▁▁▁
epc_C	0	1.00	128.94	78.50	18.00	74.00	108.00	161.00	688.00	▇▃▁▁▁
epc_D	0	1.00	163.42	47.81	4.00	134.00	160.00	185.00	413.00	▁▇▅▁▁
epc_E	0	1.00	55.72	32.21	0.00	34.00	50.00	72.00	243.00	▇▇▁▁▁
epc_F	0	1.00	13.26	16.59	0.00	4.00	8.00	16.00	196.00	▇▁▁▁▁
epc_G	0	1.00	3.63	6.04	0.00	0.00	2.00	4.00	96.00	▇▁▁▁▁
IMD_Decile	0	1.00	6.47	2.78	1.00	4.00	7.00	9.00	10.00	▂▅▅▆▇
IMDScore	0	1.00	16.77	12.38	0.95	7.14	13.39	22.90	72.08	▇▅▂▁▁

## 
## Basingstoke and Deane        East Hampshire             Eastleigh 
##                   109                    72                    77 
##               Fareham               Gosport                  Hart 
##                    73                    53                    57 
##                Havant         Isle of Wight            New Forest 
##                    78                    89                   114 
##            Portsmouth              Rushmoor           Southampton 
##                   125                    58                   148 
##           Test Valley            Winchester 
##                    71                    70

## [1] 1194

Now we need to convert the per capita to totals and then use the number of electricity meters as a proxy for the number of dwellings

Ideally we’d have Census 2021 data but we don’t have it yet. So instead we’ll use the number of electricity meters for 2018 which aligns with the CREDS data (might be an over-estimate if a dwelling has 2…)

First check the n electricity meters logic…

## LSOAs (check):

## [1] 1194

## [1] 1194

Check that the number of electricity meters reasonably correlates with the number of EPCs from the CREDS data. We would not expect the number of gas meters to correlate due to non-gas dwellings etc.

That assumption seems sensible…

4.2.1 Estimate per dwelling emissions

We want to present the analysis in ‘per dwelling’ or ‘per household’ terms so we need to convert the total kg CO2e values to per dwelling values by dividing by the number of electricity meters.

## # Summary of per dwelling values

Table 4.3: Data summary

Name	…[]
Number of rows	1194
Number of columns	9
Key	NULL
_______________________
Column type frequency:
numeric	9
________________________
Group variables	None

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
CREDStotal_kgco2e_pdw	0	1	21834.16	8865.44	5802.13	15089.90	20566.08	27272.40	52773.03	▅▇▅▂▁
CREDSgas_kgco2e2018_pdw	0	1	2150.21	726.47	0.00	1784.09	2175.64	2624.55	4333.54	▁▂▇▃▁
CREDSelec_kgco2e2018_pdw	0	1	1072.63	243.32	582.86	933.02	1012.61	1115.83	2272.31	▂▇▁▁▁
CREDSmeteredHomeEnergy_kgco2e2018_pdw	0	1	3222.84	728.77	1123.58	2738.86	3166.69	3689.29	6404.30	▁▇▆▁▁
CREDSotherEnergy_kgco2e2011_pdw	0	1	180.66	404.84	0.00	32.77	54.67	103.88	2984.01	▇▁▁▁▁
CREDSallHomeEnergy_kgco2e2018_pdw	0	1	3403.50	737.23	1506.11	2873.42	3305.60	3858.38	6790.95	▂▇▅▁▁
CREDScar_kgco2e2018_pdw	0	1	2621.81	959.27	457.84	1909.43	2523.53	3295.55	5174.83	▂▇▇▅▂
CREDSvan_kgco2e2018_pdw	0	1	409.99	1109.82	22.61	180.18	255.48	394.30	32186.23	▇▁▁▁▁
CREDSpersonalTransport_kgco2e2018_pdw	0	1	3031.80	1488.24	528.81	2144.14	2885.36	3714.77	33215.59	▇▁▁▁▁

Examine patterns of per dwelling emissions for sense.

4.2.1.1 All emissions

Figure 4.3 shows the LSOA level per dwelling ‘all emissions’ in Tonnes CO2e as estimated by the CREDS tool against the Index of Multiple Deprivation (IMD) score and uses the size of the points to represent the % of dwellings with electric heating. Colour is used to represent the IMD decile where decile 1 is the 10% least deprived.

## Per dwelling T CO2e - all emissions

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 4.3: Scatter of LSOA level all per dwelling emissions against IMD score

## Correlation test (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$IMDScore and credsLsoaDT$CREDStotal_kgco2e_pdw
## t = -28.11, df = 1192, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6643148 -0.5959946
## sample estimates:
##        cor 
## -0.6313783

##    LAD11NM             WD18NM            LSOA11CD         All_Tco2e_per_dw
##  Length:1194        Length:1194        Length:1194        Min.   : 5.802  
##  Class :character   Class :character   Class :character   1st Qu.:15.090  
##  Mode  :character   Mode  :character   Mode  :character   Median :20.566  
##                                                           Mean   :21.834  
##                                                           3rd Qu.:27.272  
##                                                           Max.   :52.773

Are there any strange outliers?

4.2.1.2 Home energy use

Figure 4.4 uses the same plotting method to show emissions per dwelling due to gas use. This preserves the negative correlation shown in the previous plot for ‘all emissions’ but with some variation, notably for LSOAs which have a higher % ofelectric heating.

## Per dwelling T CO2e - gas emissions

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##       0    1784    2176    2150    2625    4334

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 4.4: Scatter of LSOA level gas per dwelling emissions against IMD score

## Correlation test (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$IMDScore and credsLsoaDT$CREDSgas_kgco2e2018_pdw
## t = -21.557, df = 1192, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5692532 -0.4875429
## sample estimates:
##        cor 
## -0.5296256

Are there any strange outliers? Could be an issue with the gas meter data to LSOA allocation…

Figure 4.5 uses the same plotting method to show emissions per dwelling due to electricity use. This is usuallu much more random… although note the LSOAs with higher % electric heating.

## Per dwelling T CO2e - elec emissions

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 4.5: Scatter of LSOA level elec per dwelling emissions against IMD score - who emits?

## Correlation test (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$IMDScore and credsLsoaDT$CREDSelec_kgco2e2018_pdw
## t = -9.5232, df = 1192, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.3178384 -0.2123733
## sample estimates:
##        cor 
## -0.2659013

Figure 4.6 uses the same plotting method to show emissions per dwelling due to other energy use. This should be higher for off-gas areas which tend to be rural areas so we also present analysis by the LSOA’s urban/rural classification.

## Per dwelling T CO2e - elec emissions

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 4.6: Scatter of LSOA level other energy per dwelling emissions against IMD score - who emits?

## Correlation test (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$IMDScore and credsLsoaDT$CREDSelec_kgco2e2018_pdw
## t = -9.5232, df = 1192, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.3178384 -0.2123733
## sample estimates:
##        cor 
## -0.2659013

Check whether all measured energy emissions combined (gas & electricity) correlate with all emissions (in this data).

## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$CREDStotal_kgco2e_pdw and credsLsoaDT$CREDSmeteredHomeEnergy_kgco2e2018_pdw
## t = 26.567, df = 1192, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.5729367 0.6442871
## sample estimates:
##       cor 
## 0.6098462

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Strong correlation. So in theory we could (currently) use measured energy emissions as a proxy for total emissions.

Repeat for all home energy - includes estimates of emissions from oil etc

## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$CREDStotal_kgco2e_pdw and credsLsoaDT$CREDSallHomeEnergy_kgco2e2018_pdw
## t = 34.154, df = 1192, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.6734136 0.7308495
## sample estimates:
##       cor 
## 0.7032774

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Slightly weaker correlation…

4.2.1.3 Transport

We don’t expect to use this data as it is already taxed in a way that relates to emissions (?)

Figure 4.7 uses the same plotting method to show emissions per dwelling due to van use. Again, we present analysis by the LSOA’s urban/rural classification.

## Per dwelling T CO2e - car emissions

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 4.7: Scatter of LSOA level car use per dwelling emissions against IMD score

## Correlation with IMD score (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$IMDScore and credsLsoaDT$CREDScar_kgco2e2018_pdw
## t = -30.024, df = 1192, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6873457 -0.6226502
## sample estimates:
##        cor 
## -0.6562022

##                          RUC11 mean_car_kgco2e mean_van_kgco2e
## 1:       Rural town and fringe        3085.434        370.1960
## 2: Rural village and dispersed        3977.615        745.3948
## 3:         Urban city and town        2445.047        382.8929

Figure 4.8 uses the same plotting method to show emissions per dwelling due to van use.

## Per dwelling T CO2e - van emissions

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 4.8: Scatter of LSOA level van use per dwelling emissions against IMD score

## Correlation with IMD score (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$IMDScore and credsLsoaDT$CREDSvan_kgco2e2018_pdw
## t = 0.99035, df = 1192, p-value = 0.3222
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.02810456  0.08526582
## sample estimates:
##        cor 
## 0.02867284

Are there any strange outliers? Could be an issue with the CREDS model…

4.2.2 Impute EPC counts

In order to estimate the LSOA level retrofit costs, we need to impute the EPC counts in each LSOA. We do this using the number of electricity meters as the presumed number of dwellings and the observed % of EPCs in each band for all dwellings with EPCs which is provided by the CREDS data. This assumes that if we had EPCs for all dwellings then the % in each band in each LSOA would stay the same. This is quite a bold assumption…

Note that the EPC database is continuously updated so more recent upgrades will not be captured in the data used for this analysis. This means the total retrofit costs are likely to be an over-estimate. The extent of this over-estimate would require the use of an updated (current) EPC data extract and is left for future work.

## N EPCs

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   153.0   309.0   376.0   422.6   492.0  2350.0

## N elec meters

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   325.0   619.2   691.0   731.8   802.0  2638.0

Correlation between high % EPC F/G or A/B and deprivation?

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Now we need to convert the % to dwellings using the number of electricity meters (see above).

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

4.2.3 Estimating the annual emissions levy

Case studies:

• Annual carbon tax
• Half-hourly (real time) carbon tax (not implemented) - this would only affect electricity

BEIS/ETC Carbon ‘price’

• 2020 - £21.93 https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/849276/Carbon__Penalty_Price_Determination_for_2020.pdf

EU carbon ‘price’

• September 2021: €60 (£51) https://tradingeconomics.com/eecxm:ind

BEIS Carbon ‘Value’ https://www.gov.uk/government/publications/valuing-greenhouse-gas-emissions-in-policy-appraisal/valuation-of-greenhouse-gas-emissions-for-policy-appraisal-and-evaluation#annex-1-carbon-values-in-2020-prices-per-tonne-of-co2

• based on a Marginal Abatement Cost (MAC)
• 2021:
  • Low: £122/T
  • Central: £245/T <- use the central value for now
  • High: £367/T

Scenario 1: apply the central value Scenario 2: apply the low/central/high as a rising block tariff for each emissions source. Set threhsolds to 33% and 66% (in absence of any other guidance!)

4.2.3.1 Scenario 1: central cost

The table below shows the overall £ GBP total for the case study area in £M.

##    beis_GBPtotal_c beis_total_c_gas beis_GBPtotal_c_elec
## 1:          4441.5            451.4               228.82

The table below shows the mean per dwelling value rounded to the nearest £10.

##    beis_GBPtotal_c_perdw beis_GBPtotal_c_gas_perdw beis_GBPtotal_c_elec_perdw
## 1:                  5350                       530                        260
##    beis_GBPtotal_c_energy_perdw
## 1:                          790

Figure ?? shows the total £k per LSOA and £ per dwelling revenue using BEIS central carbon price plotted against IMD score. The tables show the LSOAs with the highest and lowest values.

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.9: £k per LSOA revenue using BEIS central carbon price

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.10: £k per LSOA revenue using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1422    3697    5039    5349    6682   12929

Figure ?? repeats the analysis but just for gas.

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.11: £k per LSOA incurred via gas using BEIS central carbon price

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.12: £k per LSOA incurred via gas using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     0.0   437.1   533.0   526.8   643.0  1061.7

Figure ?? repeats the analysis for electricity.

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.13: £k per LSOA incurred via electricity using BEIS central carbon price

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.14: £k per LSOA incurred via electricity using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   142.8   228.6   248.1   262.8   273.4   556.7

Figure ?? shows the same analysis for metered energy (elec + gas)

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.15: £k per LSOA incurred via electricity and gas using BEIS central carbon price

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.16: £k per LSOA incurred via electricity and gas using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   275.3   671.0   775.8   789.6   903.9  1569.1

Figure ?? shows the same analysis for all home energy (elec + gas + oil etc)

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.17: £k per LSOA incurred via all home heat energy using BEIS central carbon price

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.18: £k per LSOA incurred via all home heat energy using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   275.3   671.0   775.8   789.6   903.9  1569.1

4.2.3.2 Scenario 2: Rising block tariff

Applied at to per dwelling values (not LSOA total)

Cut at 25%, 50% - so any emissions over 50% get high carbon cost

## Cuts for total per dw

##        0%       25%       50%       75%      100% 
##  5802.128 15089.898 20566.083 27272.399 52773.034

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

##           V1 beis_GBPtotal_sc2_l_perdw beis_GBPtotal_sc2_c_perdw
##  1: 19.50899                  1840.968                 1082.6772
##  2: 19.82781                  1840.968                 1160.7888
##  3: 13.99627                  1707.545                    0.0000
##  4: 20.65559                  1840.968                 1341.6653
##  5: 24.81776                  1840.968                 1341.6653
##  6: 23.21860                  1840.968                 1341.6653
##  7: 16.50536                  1840.968                  346.7874
##  8: 12.62994                  1540.852                    0.0000
##  9: 18.11356                  1840.968                  740.7964
## 10: 17.63948                  1840.968                  624.6469
##     beis_GBPtotal_sc2_h_perdw beis_GBPtotal_sc2_perdw
##  1:                   0.00000                2923.645
##  2:                   0.00000                3001.756
##  3:                   0.00000                1707.545
##  4:                  32.84875                3215.482
##  5:                1560.36436                4742.997
##  6:                 973.47545                4156.108
##  7:                   0.00000                2187.755
##  8:                   0.00000                1540.852
##  9:                   0.00000                2581.764
## 10:                   0.00000                2465.615

Table 4.10: Data summary

Name	…[]
Number of rows	1194
Number of columns	3
Key	NULL
_______________________
Column type frequency:
numeric	3
________________________
Group variables	None

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
V1	0	1	21.83	8.87	5.80	15.09	20.57	27.27	52.77	▅▇▅▂▁
beis_GBPtotal_sc2_perdw	0	1	4107.16	2776.24	707.86	1842.36	3183.22	5643.85	15002.58	▇▃▂▁▁
beis_GBPtotal_sc2	0	1	2764975.74	1564529.45	587844.80	1514526.30	2355174.80	3775189.52	7035326.91	▇▆▅▂▂

##    nLSOAs sum_total_sc1 sum_total_sc2
## 1:   1194      4441.497      3301.381

##    CREDSgas_kgco2e2018_pdw beis_GBPgas_sc2_l_perdw
## 1:                2001.180                217.6592
## 2:                2192.751                217.6592
## 3:                2034.557                217.6592
## 4:                2154.426                217.6592
## 5:                2240.187                217.6592
## 6:                2316.899                217.6592

##    CREDSgas_kgco2e2018_pdw beis_GBPgas_sc2_l_perdw beis_GBPgas_sc2_c_perdw
## 1:                2001.180                217.6592                53.18664
## 2:                2192.751                217.6592                95.92818
## 3:                2034.557                217.6592                61.36401
## 4:                2154.426                217.6592                90.73196
## 5:                2240.187                217.6592                95.92818
## 6:                2316.899                217.6592                95.92818
##    beis_GBPgas_sc2_h_perdw beis_GBPgas_sc2_perdw
## 1:                0.000000              270.8458
## 2:                6.281471              319.8688
## 3:                0.000000              279.0232
## 4:                0.000000              308.3911
## 5:               23.690540              337.2779
## 6:               51.843957              365.4313

## [1] 301.6514

## [1] 153.2484

##    nLSOAs sumAllConsEmissions_GBP sumGasEmissions_GBP sumElecEmissions_GBP
## 1:   1194                3301.381            301.6514             153.2484

4.2.4 Estimate retofit costs

• from A-E <- 13300
• from F-G <- 26800

Excludes EPC A, B & C (assumes no need to upgrade)

## To retrofit D-E (£m)

## [1] 6259.381

## Number of dwellings: 470630

## To retrofit F-G (£m)

## [1] 961.2963

## Number of dwellings: 35869

## To retrofit D-G (£m)

## [1] 7220.677

## To retrofit D-G (mean per dwelling £k)

## [1] 14.14921

##    meanRetrofitPerLSOA totalRetrofit
## 1:            6047.468    7220677184

Plot retrofit costs

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Map retrofit costs

Figure 4.19: LSOA retrofit costs (upgrade EPC C to F)

4.2.5 Compare levy with costs

4.2.5.1 Scenario 1

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Repeat per dwelling

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

4.2.5.2 Scenario 2

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Repeat per dwelling

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

4.2.6 Years to pay…

4.2.6.1 Scenario 1

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.062   2.097   2.789   3.162   3.867  10.307

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   9.215  15.360  17.941  19.020  20.921  57.639

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

What happens in Year 2 totally depends on the rate of upgrades…

4.2.6.2 Scenario 2

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.924   2.502   4.376   5.341   7.663  20.699

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   9.215  15.360  17.941  19.020  20.921  57.639

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

What happens in Year 2 totally depends on the rate of upgrades…

4.2.6.3 Compare scenarios

Figure 4.20 compares the total levy generated by district.

Figure 4.20: Compare levy at district level

Figure 4.21

Figure 4.21: Compare districts

Figure 4.22

Figure 4.22: Pay back scatter for all LSOAs

5 R environment

5.1 R packages used

• base R (R Core Team 2016)
• bookdown (Xie 2016a)
• data.table (Dowle et al. 2015)
• ggplot2 (Wickham 2009)
• kableExtra (Zhu 2018)
• knitr (Xie 2016b)
• rmarkdown (Allaire et al. 2018)
• skimr (Arino de la Rubia et al. 2017)

5.2 Session info

6 Data Tables

I don’t know if this will work…

## Doesn't

References

Allaire, JJ, Yihui Xie, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, Hadley Wickham, Joe Cheng, and Winston Chang. 2018. Rmarkdown: Dynamic Documents for r. https://CRAN.R-project.org/package=rmarkdown.

Arino de la Rubia, Eduardo, Hao Zhu, Shannon Ellis, Elin Waring, and Michael Quinn. 2017. Skimr: Skimr. https://github.com/ropenscilabs/skimr.

Dowle, M, A Srinivasan, T Short, S Lianoglou with contributions from R Saporta, and E Antonyan. 2015. Data.table: Extension of Data.frame. https://CRAN.R-project.org/package=data.table.

R Core Team. 2016. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Wickham, Hadley. 2009. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. http://ggplot2.org.

Xie, Yihui. 2016a. Bookdown: Authoring Books and Technical Documents with R Markdown. Boca Raton, Florida: Chapman; Hall/CRC. https://github.com/rstudio/bookdown.

———. 2016b. Knitr: A General-Purpose Package for Dynamic Report Generation in r. https://CRAN.R-project.org/package=knitr.

Zhu, Hao. 2018. kableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax. https://CRAN.R-project.org/package=kableExtra.